LLMs

Language Models in Production: Challenges and Opportunities Language models in production are powerful tools, but they demand careful operations. In real systems, you must plan for reliability, safety, and ongoing governance. This article highlights common hurdles and practical opportunities for teams that deploy AI at scale. Common challenges include the following: Latency and uptime: users expect fast answers; plan for robust infrastructure, caching, and fallbacks. Privacy and security: protect sensitive data and control who can access it. Bias, safety, and governance: monitor outputs, enforce policies, and document decisions. Data drift and versioning: prompts and inputs can drift; track changes and retrain when needed. On the flip side, production models offer opportunities: faster iteration, better user experience, and scalable support. With guardrails and monitoring, teams can improve quality while reducing risk. Automation in testing, rollout, and rollback helps maintain momentum. ...

Artificial intelligence foundations for developers Building AI features starts with a clear problem and honest constraints. Developers benefit from a simple map: what to know, what to measure, and how to ship safely. This article covers fundamental ideas that help you create reliable AI-powered apps. Core concepts Training vs inference: training tunes a model once; inference runs it to answer many requests. Data quality: good data improves results; biased or noisy data hurts outcomes. Evaluation: pick metrics that reflect user value, not only raw accuracy. Latency and cost: response time and compute price affect the user experience. Transfer learning: reuse existing models to save time and improve results. Data matters Data drives AI behavior. Use clean, representative data and protect user privacy. Minimize data collection, label thoughtfully, and document data sources. If data shifts, you may need to adjust prompts, fine-tune, or update the model version. ...